Crowdsourcing is one solution to solve a large data problem by breaking it into smaller tasks that can be completed by an individual. Once the smaller tasks are completed by individuals, the large data problem will be completed. A problem with using crowdsourcing to solve large data problems is that the smaller tasks may not be completed correctly, and thus, the large data problem will not be completed correctly.
It often is desirable to obtain and analyze a very large number of data points in order to have normalized or expected values of data. However, obtaining the large number of data points comes at a cost. For example, having receiving input quickly to achieve normalized or expected data may be impractical as the input from the crowd may not be received quick enough. Moreover, the input from the crowd may be imprecise and may cause deviations that consume even more time to move back towards normalization or expected values.
To increase speed of input from the crowd, in some instances the crowd may be compensated. However, the costs of this input can be cost prohibitive. For example, this requires a large compensation commitment to compensate the crowd for their input. However, even with the compensation, the results may still be unacceptable or outside of what are expected data points. Hence, in addition to being cost prohibitive, there is a waste of time and resources for data points that are unusable.
Moreover, there are issues with manually pairing individuals and tasks to be completed within short time frames. This process also may be time consuming, inefficient, and costly. If received data is not analyzed quickly to determine whether it is within a proper margin of expected results, the data ultimately may be unusable and require redoing the tasks.